various enhancements #1
Description
great works!
debug these math expression code is a very big problem
not only scalar vector matrix , high rank tensor support
Visualizing 3D tensors and beyond:
TensorDim is contain : left to right [N,C,H,W] N->C->H->W
TensorDim like flatten Onions,cabbage box in box represent dim
TensorDim has real represention information (N,C,H,W etc.)
namedtensor support
plaidml dsl
Types Tensor TensorDim TensorIndex
represent actual data:
audio, 1D plot, text : vector
image ,2D plot : matrix
etc.
expression graph:
different color input variable ,leaf parameters/variable
with temporal variable / activation : "Road width"
elementwise leaf / elementwise or slice or other connection(edge) print
AST forward & backward mode
animation:
expression step computation mode (debug)
slice ,reshape, .T() other manipulate N-d array TensorDim/TensorIndex operators
example : matmul = m@v
m@v = (v.squeeze(0).expand_as(w) * w).sum(1,keepdim=True).unsqueeze(1)
more ... conv2d etc
interactive:
interactive build block(visual programing)
reverse interactive(debug) : selected tensor elements and follow it in expression graph
selected element : muti view , like convolution-visualizer Input(Inputgrad) Weight(Weightgrad) Output(Outputgrad) views
NN support:
NN module visualization (conv2d)
bigger computation graph : pytorchrec
multi computation graph visualization and live debug
other useful link
SimpNet memory map
Visualize the virtual address space of a Windows process on a Hilbert curve.
